Fault Diagnosis of Lithium-ion Battery Pack Based on Optimized Support Vector Machine Algorithm
DOI:
https://doi.org/10.13052/dgaej2156-3306.4043Keywords:
SVM, lithium ion, battery pack, internal monomer, aging fault diagnosisAbstract
Diagnosing the faults of lithium-ion battery packs is beneficial for improving the accuracy and efficiency of battery pack fault diagnosis, and promoting the safety and reliability of battery packs. A machine learning-based fault diagnosis method is proposed to address the significant limitations of traditional sensor data monitoring. Based on the support vector machine algorithm for classification and using a simulated annealing algorithm to optimize its parameters, a fault diagnosis system for internal individual aging of battery packs is established. The results indicated that the research method had the highest diagnostic accuracy, and its diagnostic performance was optimal at a temperature of 25∘C. The number of false positives at temperatures of 25∘C, 10∘C, and −10∘C was 0, 1, and 1, respectively. In the fault diagnosis of aging monomers 3, 7, and 11 within the battery pack, the diagnostic accuracy of the research method was 99.76%, 100%, and 99.64%, respectively. The system demonstrated an ability to accurately differentiate between faulty and non-faulty units of the battery pack, a capability that was consistent with the actual situation. Its diagnostic response time was also found to be rapid, with an average of 2 seconds. The system’s efficacy in real-time performance is conducive to the timely diagnosis and management of faults in electric vehicle lithium-ion battery packs, thereby mitigating safety hazards.
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